ROTATION FORESTS AND RANDOM FOREST CLASSIFIERS FOR MONITORING OF VEGETATION IN PAYS DE BREST (FRANCE)

Abstract. Remote sensing is a potentially very useful source of information for spatial monitoring of natural or cultivated vegetation. The latest advances, in particular the arrival of new image acquisition programs, are changing the temporal approach to monitoring vegetation. The latest European satellites launched, delivering an image every 5 days for each point on the globe, allow the end of a growing season to be monitored. The main objective of this work is to identify and map the vegetation in the Pays de Brest area by using a multi sensors stacking of Sentinel-1 and Sentinel-2 satellites data via Random Forest, Rotation forests (RoF) and Canonical Correlation Forests (CCFs). RoF and CCF create diverse base learners using data transformation and subset features. Twenty four radar images and optical dataa representing different dates in 2017 were processed in time series stacks. The results of RoF and CCF were compared with the ones of RF.